Analysing the American National Election Study of 1948 with memisc

Introduction

This vignette gives an example for the analysis of a typical social science data set. It is the data file of the American National Election Study of 19481 available from the American National Election Studies website. The data file contains data from to USA-wide surveys conducted October and November 1948 by the Survey Research Centre, University Michigan (principal investigators: Angus Campbell and Robert L. Kahn). The total number of cases in the data set is 662 and the number of variables is 65 (more details about this data set can be found at http://www.electionstudies.org/studypages/1948prepost/1948prepost.htm).

With 662 cases and 65 variables, the 1948 ANES data set is relatively small as compared to current social science data sets. Such larger data sets can be processed along the same lines as in this vignette. Unlike the 1948 ANES data, their size as well as, in some cases, legal restrictions prohibit the inclusion of such a data set into the package, however.

This vignette starts with a demonstration how a data file can be examined before loading it and how a subset of the data can be loaded into memory. After loading this subset into memory, some descriptive analyses are conducted that showcase the construction of contingency tables and of general tables of descriptive statistics using the genTable function. In addition, a logit analysis is demonstrated and the collection of several logit coefficients into a comprehensive table by the mtable function.

It should be noted that the analyses reported in the following are conducted only for purpose of demonstrating the features of the package and are not to be considered of conclusive scientific evidence of any kind.

This vignette is run with the help of the knitr package. This allows to showcase not only data management facilities provided by memisc. The following code also demonstrates how output created with some of the facilities of memisc can neatly integrated in reports generated with knitr. Before we start, we adjust knitr’s output (with which this vignette is formatted) to produce HTML where possible.

knit_print.codebook <-function(x,...) 
  knitr::asis_output(format_html(x,...))

knit_print.descriptions <-function(x,...) 
  knitr::asis_output(format_html(x,...))

knit_print.ftable <-function(x,options,...)
  knitr::asis_output(
    format_html(x,
                digits=if(length(options$ftable.digits))
                          options$ftable.digits
                       else 0,
                ...))
# We can now adjust the number of digits after the comma
# for each column e.g. by adding an `ftable.digits` option
# to an R chunk, as in ```{r,ftable=c(2,2,0)}

knit_print.mtable <-function(x,...)
  knitr::asis_output(format_html(x,...))

Reading in a “portable” SPSS data file

We start with importing the data into R. The following code extracts the SPSS portable file NES1948.POR from zip file NES1948.ZIP delivered with the memisc package.

library(memisc)
options(digits=3)
nes1948.por <- unzip(system.file("anes/NES1948.ZIP",package="memisc"),
                     "NES1948.POR",exdir=tempfile())

Now the portable file is in a temporary directory and the path to the file is contained in the string variable nes1948.por. In the next step, the file is declared as a SPSS/PSPP portable file using the function spss.portable.file, which as first argument takes the path to the file. spss.portable.file reads in the information about the variables contained in the data set and counts the number of cases in the file. That is, standard I/O operations are used on the file, but the data read in are just thrown away without allocating core memory for the data. This counting of cases can, of course, be suppressed if it would take too long.

nes1948 <- spss.portable.file(nes1948.por)
Warning: Duplicate labels 'NAME NOT KNOWN'
Warning: Duplicate labels 'HAVE HEARD OF TAFT-HARTLEY ACT'
Warning: Duplicate labels 'DID NOT CONSIDER ANYONE ELSE' 'CONSIDERED
WALLACE' 'CONSIDERED OTHER' 'NA' 'CONSIDERED TRUMAN'
Warning: Duplicate labels 'DISAGREED WITH PLATFORM OR POLICY - TO'

Warning: Duplicate labels 'DISAGREED WITH PLATFORM OR POLICY - TO'
Warning: Duplicate labels 'RENT CONTROL' 'PRICE CONTROL' 'TAFT-HARTLEY'
'FARM PRICES AND SUPPORT' 'LOWER INCOME TAX' 'CIVIL RIGHTS' 'BALANCE
BUDGET' '(GOVERNMENT) HOUSING' 'DEFENSE ACTIVITY' 'GOVERNMENT ATOMIC
CONTROL' 'NEW DEAL' 'MARSHALL PLAN' 'FIRM RUSSIAN POLICY' 'HELP ISRAEL
(PALESTINE)' 'PROMOTE PEACE'

Warning: Duplicate labels 'RENT CONTROL' 'PRICE CONTROL' 'TAFT-HARTLEY'
'FARM PRICES AND SUPPORT' 'LOWER INCOME TAX' 'CIVIL RIGHTS' 'BALANCE
BUDGET' '(GOVERNMENT) HOUSING' 'DEFENSE ACTIVITY' 'GOVERNMENT ATOMIC
CONTROL' 'NEW DEAL' 'MARSHALL PLAN' 'FIRM RUSSIAN POLICY' 'HELP ISRAEL
(PALESTINE)' 'PROMOTE PEACE'

Warning: Duplicate labels 'RENT CONTROL' 'PRICE CONTROL' 'TAFT-HARTLEY'
'FARM PRICES AND SUPPORT' 'LOWER INCOME TAX' 'CIVIL RIGHTS' 'BALANCE
BUDGET' '(GOVERNMENT) HOUSING' 'DEFENSE ACTIVITY' 'GOVERNMENT ATOMIC
CONTROL' 'NEW DEAL' 'MARSHALL PLAN' 'FIRM RUSSIAN POLICY' 'HELP ISRAEL
(PALESTINE)' 'PROMOTE PEACE'

Warning: Duplicate labels 'RENT CONTROL' 'PRICE CONTROL' 'TAFT-HARTLEY'
'FARM PRICES AND SUPPORT' 'LOWER INCOME TAX' 'CIVIL RIGHTS' 'BALANCE
BUDGET' '(GOVERNMENT) HOUSING' 'DEFENSE ACTIVITY' 'GOVERNMENT ATOMIC
CONTROL' 'NEW DEAL' 'MARSHALL PLAN' 'FIRM RUSSIAN POLICY' 'HELP ISRAEL
(PALESTINE)' 'PROMOTE PEACE'
print(nes1948)

SPSS portable file '/tmp/RtmpDaQ7Za/file731548013b73/NES1948.POR' 
    with 67 variables and 662 observations

At this stage, the data are not loaded into the memory yet. But we can see which variables exist inside the data set:

names(nes1948)
 [1] "vversion" "vdsetno"  "v480001"  "v480002"  "v480003"  "v480004" 
 [7] "v480005"  "v480006"  "v480007"  "v480008"  "v480009"  "v480010" 
[13] "v480011"  "v480012"  "v480013"  "v480014a" "v480014b" "v480015a"
[19] "v480015b" "v480016a" "v480016b" "v480017a" "v480017b" "v480018" 
[25] "v480019"  "v480020"  "v480021a" "v480021b" "v480022a" "v480022b"
[31] "v480023"  "v480024"  "v480025a" "v480025b" "v480026"  "v480027" 
[37] "v480028"  "v480029"  "v480030"  "v480031a" "v480031b" "v480031c"
[43] "v480032a" "v480032b" "v480032c" "v480033a" "v480033b" "v480034a"
[49] "v480034b" "v480035a" "v480035b" "v480036a" "v480036b" "v480037" 
[55] "v480038"  "v480039"  "v480040"  "v480041"  "v480042"  "v480043" 
[61] "v480044"  "v480045"  "v480046"  "v480047"  "v480048"  "v480049" 
[67] "v480050"

Note that the variable names are all changed from uppercase to lowercase (SPSS does not distinguish uppercase and lowercase variable names and uppercase looks like shouting). Casefolding could have been suppressed by the call spsp.portable.file(nes1948.por,tolower=FALSE).

We also can ask for a description (“variable label”) for each variable:

description(nes1948)
Variable Description
vversion NES VERSION NUMBER
vdsetno NES DATASET NUMBER
v480001 ICPSR ARCHIVE NUMBER
v480002 INTERVIEW NUMBER
v480003 POP CLASSIFICATION
v480004 CODER
v480005 NUMBER OF CALLS TO R
v480006 R REMEMBER PREVIOUS INT
v480007 INTR INTERVIEW THIS R
v480008 PRVS PRE-ELCTN R REINT
v480009 R INT IN PRE/POSTELCTN
v480010 RENT CNTRL KEPT/DROPPED
v480011 GOVT CONTROL PRICES
v480012 WHAT TO DO W TFT-HT ACT
v480013 PRESLELCTN OTCM SURPRISE
v480014a WHY PPL VTD FOR TRUMAN 1
v480014b WHY PPL VTD FOR TRUMAN 2
v480015a WHY PPL VTD AGNST TRUMAN 1
v480015b WHY PPL VTD AGNST TRUMAN 2
v480016a WHY PPL VTD FOR DEWEY 1
v480016b WHY PPL VTD FOR DEWEY 2
v480017a WHY PPL VTD AGNST DEWEY 1
v480017b WHY PPL VTD AGNST DEWEY 2
v480018 DID R VOTE/FOR WHOM
v480019 WN DECIDE FOR WHOM TO VT
v480020 CNSD VT FOR SOMEONE ELSE
v480021a XWHY DID NOT VT FOR HIM 1
v480021b XWHY DID NOT VT FOR HIM 2
v480022a WHY VT THE WAY YOU DID 1
v480022b WHY VT THE WAY YOU DID 2
v480023 VOTED STRAIGHT TICKET
v480024 R NOT VT-IF VT,FOR WHOM
v480025a R NOT VT-WHY DID NOT VT 1
v480025b R NOT VT-WHY DID NOT VT 2
v480026 R NOT VT-WAS R REG TO VT
v480027 VTD IN PRVS PRESL ELCTN
v480028 VTD FOR WHOM IN 1944
v480029 OCCUPATION OF HEAD
v480030 HEAD BELONG TO LBR UN
v480031a GRPS IDENTIFIED W TRUMAN 1
v480031b GRPS IDENTIFIED W TRUMAN 2
v480031c GRPS IDENTIFIED W TRUMAN 3
v480032a GRPS IDENTIFIED W DEWEY 1
v480032b GRPS IDENTIFIED W DEWEY 2
v480032c GRPS IDENTIFIED W DEWEY 3
v480033a ISSUES CONNECTED W TRMN 1
v480033b ISSUES CONNECTED W TRMN 2
v480034a ISSUES CONNECTED W DEWEY 1
v480034b ISSUES CONNECTED W DEWEY 2
v480035a PERSONAL ATTRIBUTE TRMN 1
v480035b PERSONAL ATTRIBUTE TRMN 2
v480036a PERSONAL ATTRIBUTE DEWEY 1
v480036b PERSONAL ATTRIBUTE DEWEY 2
v480037 CMPN INCIDENTS MENTIONED
v480038 41-PRESLELCTN PLAN TO VT
v480039 41-PLAN TO VT REP/DEM
v480040 41-USA’S CNCRN W OTHERS
v480041 41-SATISD USA TWRD RUSS
v480042 41-INFORMATION LEVEL
v480043 41-USA GV IN,AGRT RUSS
v480044 41-USA-RUSS AGRT VIA U.N
v480045 SEX OF RESPONDENT
v480046 RACE OF RESPONDENT
v480047 AGE OF RESPONDENT
v480048 EDUCATION OF RESPONDENT
v480049 TOTAL 1948 INCOME
v480050 RELIGIOUS PREFERENCE

or even a code book using

codebook(nes1948)

(this is not shown here because the output would have taken more then thirty pages). We can also get a codebook of the first few variabels instead, with

codebook(nes1948[1:5])

vversion — ‘NES VERSION NUMBER’

Storage mode: double
Measurement: interval

Min: 1 . 000
Max: 1 . 000
Mean: 1 . 000
Std.Dev.: 0 . 000
Skewness: NaN
Kurtosis: NaN

vdsetno — ‘NES DATASET NUMBER’

Storage mode: character
Measurement: nominal

Values and labels N Percent
(unlab.vld.) 662 100 . 0 100 . 0

v480001 — ‘ICPSR ARCHIVE NUMBER’

Storage mode: double
Measurement: interval

Min: 7218 . 000
Max: 7218 . 000
Mean: 7218 . 000
Std.Dev.: 0 . 000
Skewness: NaN
Kurtosis: NaN

v480002 — ‘INTERVIEW NUMBER’

Storage mode: double
Measurement: interval

Min: 1001 . 000
Max: 1662 . 000
Mean: 1331 . 500
Std.Dev.: 191 . 103
Skewness: 0 . 000
Kurtosis: -1 . 200

v480003 — ‘POP CLASSIFICATION’

Storage mode: double
Measurement: nominal

Values and labels N Percent
1 ‘METROPOLITAN AREA’ 182 27 . 5 27 . 5
2 ‘TOWN OR CITY’ 354 53 . 5 53 . 5
3 ‘OPEN COUNTRY’ 126 19 . 0 19 . 0

Reading in a subset of the data

After we have decided which variables to use we can read in a subset of the data:

vote.48 <- subset(nes1948,
              select=c(
                  v480018,
                  v480029,
                  v480030,
                  v480045,
                  v480046,
                  v480047,
                  v480048,
                  v480049,
                  v480050
                  ))

The subset of the ANES 1948 we read in is now contained in the variable vote.48, which contains an object of class data.set. A data.set is an “embellished” version of a data.frame, a data structure intended to contained labelled vectors. labelled vectors contain the all the special information attached to the variables in the original data set, such as variable labels, value labels, and general missing values. A short summary of this special information shows up after a call to str.

str(vote.48)
Data set with 662 obs. of 9 variables:
 $ v480018: Nmnl. item w/ 7 labels for 1,2,3,... + ms.v.  num  1 2 1 2 1 2 2 1 2 1 ...
 $ v480029: Nmnl. item w/ 12 labels for 10,20,30,... + ms.v.  num  70 30 40 10 10 20 80 80 40 40 ...
 $ v480030: Nmnl. item w/ 4 labels for 1,2,8,... + ms.v.  num  1 2 2 2 2 2 2 2 1 1 ...
 $ v480045: Nmnl. item w/ 3 labels for 1,2,9 + ms.v.  num  1 2 2 2 1 2 1 2 1 1 ...
 $ v480046: Nmnl. item w/ 4 labels for 1,2,3,... + ms.v.  num  1 1 1 1 1 1 1 1 1 1 ...
 $ v480047: Nmnl. item w/ 7 labels for 1,2,3,... + ms.v.  num  3 3 2 3 2 3 4 5 2 2 ...
 $ v480048: Nmnl. item w/ 4 labels for 1,2,3,... + ms.v.  num  1 2 2 3 3 2 1 1 2 2 ...
 $ v480049: Nmnl. item w/ 8 labels for 1,2,3,... + ms.v.  num  4 7 5 7 5 7 5 2 5 6 ...
 $ v480050: Nmnl. item w/ 6 labels for 1,2,3,... + ms.v.  num  1 1 2 1 2 1 1 1 1 2 ...

This output shows, for example, that variable v480018 has the description (variable label) “DID R VOTE/FOR WHOM” is considered as having nominal level of measurement, has seven value labels and one defined missing value.

Since the variable names in the ANES data set are not very mnemonic, we rename the variables:

vote.48 <- rename(vote.48,
                  v480018 = "vote",
                  v480029 = "occupation.hh",
                  v480030 = "unionized.hh",
                  v480045 = "gender",
                  v480046 = "race",
                  v480047 = "age",
                  v480048 = "education",
                  v480049 = "total.income",
                  v480050 = "religious.pref"
        )

Since many data sets available from public repositories have such non-mnemonic variable names as in this example, it might be convenient to do the data loading and renaming in one step. Indeed it is possible:

vote.48 <- subset(nes1948,
                  select=c(
                    vote           = v480018,
                    occupation.hh  = v480029,
                    unionized.hh   = v480030,
                    gender         = v480045,
                    race           = v480046,
                    age            = v480047,
                    education      = v480048,
                    total.income   = v480049,
                    religious.pref = v480050
                  ))

Before we start with analyses, we take a closer look at the data.

codebook(vote.48)

vote — ‘DID R VOTE/FOR WHOM’

Storage mode: double
Measurement: nominal
Missing values: 9

Values and labels N Percent
1 ‘VOTED - FOR TRUMAN’ 212 32 . 1 32 . 0
2 ‘VOTED - FOR DEWEY’ 178 27 . 0 26 . 9
3 ‘VOTED - FOR WALLACE’ 1 0 . 2 0 . 2
4 ‘VOTED - FOR OTHER’ 11 1 . 7 1 . 7
5 ‘VOTED - NA FOR WHOM’ 20 3 . 0 3 . 0
6 ‘DID NOT VOTE’ 238 36 . 1 36 . 0
9 M ‘NA WHETHER VOTED’ 2 0 . 3

occupation.hh — ‘OCCUPATION OF HEAD’

Storage mode: double
Measurement: nominal
Missing values: 99

Values and labels N Percent
10 ‘PROFESSIONAL, SEMI-PROFESSIONAL’ 44 6 . 9 6 . 6
20 ‘SELF-EMPLOYED, MANAGERIAL, SUPERVISORY’ 73 11 . 5 11 . 0
30 ‘OTHER WHITE-COLLAR (CLERICAL, SALES, ET’ 79 12 . 5 11 . 9
40 ‘SKILLED AND SEMI-SKILLED’ 164 25 . 9 24 . 8
60 ‘PROTECTIVE SERVICE’ 6 0 . 9 0 . 9
70 ‘UNSKILLED, INCLUDING FARM AND SERVICE W’ 85 13 . 4 12 . 8
80 ‘FARM OPERATORS AND MANAGERS’ 105 16 . 6 15 . 9
92 ‘STUDENT’ 7 1 . 1 1 . 1
94 ‘UNEMPLOYED’ 5 0 . 8 0 . 8
95 ‘RETIRED, TOO OLD OR UNABLE TO WORK’ 38 6 . 0 5 . 7
96 ‘HOUSEWIFE’ 28 4 . 4 4 . 2
99 M ‘NA’ 28 4 . 2

unionized.hh — ‘HEAD BELONG TO LBR UN’

Storage mode: double
Measurement: nominal
Missing values: 8-Inf

Values and labels N Percent
1 ‘YES’ 150 23 . 3 22 . 7
2 ‘NO’ 493 76 . 7 74 . 5
8 M ‘DK’ 5 0 . 8
9 M ‘NA’ 14 2 . 1

gender — ‘SEX OF RESPONDENT’

Storage mode: double
Measurement: nominal
Missing values: 9

Values and labels N Percent
1 ‘MALE’ 302 45 . 8 45 . 6
2 ‘FEMALE’ 357 54 . 2 53 . 9
9 M ‘NA’ 3 0 . 5

race — ‘RACE OF RESPONDENT’

Storage mode: double
Measurement: nominal
Missing values: 9

Values and labels N Percent
1 ‘WHITE’ 585 90 . 7 88 . 4
2 ‘NEGRO’ 60 9 . 3 9 . 1
3 ‘OTHER’ 0 0 . 0 0 . 0
9 M ‘NA’ 17 2 . 6

age — ‘AGE OF RESPONDENT’

Storage mode: double
Measurement: nominal
Missing values: 9

Values and labels N Percent
1 ‘18-24’ 57 8 . 7 8 . 6
2 ‘25-34’ 142 21 . 7 21 . 5
3 ‘35-44’ 174 26 . 6 26 . 3
4 ‘45-54’ 125 19 . 1 18 . 9
5 ‘55-64’ 86 13 . 1 13 . 0
6 ‘65 AND OVER’ 70 10 . 7 10 . 6
9 M ‘NA’ 8 1 . 2

education — ‘EDUCATION OF RESPONDENT’

Storage mode: double
Measurement: nominal
Missing values: 9

Values and labels N Percent
1 ‘GRADE SCHOOL’ 292 44 . 4 44 . 1
2 ‘HIGH SCHOOL’ 266 40 . 4 40 . 2
3 ‘COLLEGE’ 100 15 . 2 15 . 1
9 M ‘NA’ 4 0 . 6

total.income — ‘TOTAL 1948 INCOME’

Storage mode: double
Measurement: nominal
Missing values: 9

Values and labels N Percent
1 ‘UNDER $500’ 25 3 . 8 3 . 8
2 ‘$500-$999’ 43 6 . 6 6 . 5
3 ‘$1000-1999’ 110 16 . 8 16 . 6
4 ‘$2000-2999’ 185 28 . 2 27 . 9
5 ‘$3000-3999’ 142 21 . 7 21 . 5
6 ‘$4000-4999’ 66 10 . 1 10 . 0
7 ‘$5000 AND OVER’ 84 12 . 8 12 . 7
9 M ‘NA’ 7 1 . 1

religious.pref — ‘RELIGIOUS PREFERENCE’

Storage mode: double
Measurement: nominal
Missing values: 9

Values and labels N Percent
1 ‘PROTESTANT’ 460 70 . 0 69 . 5
2 ‘CATHOLIC’ 140 21 . 3 21 . 1
3 ‘JEWISH’ 25 3 . 8 3 . 8
4 ‘OTHER’ 14 2 . 1 2 . 1
5 ‘NONE’ 18 2 . 7 2 . 7
9 M ‘NA’ 5 0 . 8

We now have obtained a codebook, which contains information of the class and type of the variables in the data set, the value labels and defined missing values, and counts of the distinct values of the variables.

Analysis

Some descriptive analyses

We start our analyses with a contingency table, but first we make some preparations: We recode the variables of interest into a smaller number of categories in order to get results that are easier to read and interpret.

vote.48 <- within(vote.48,{
  vote3 <- recode(vote,
    1 -> "Truman",
    2 -> "Dewey",
    3:4 -> "Other"
    )
  occup4 <- recode(occupation.hh,
    10:20 -> "Upper white collar",
    30 -> "Other white collar",
    40:70 -> "Blue collar",
    80 -> "Farmer"
    )
  relig3 <- recode(religious.pref,
    1 -> "Protestant",
    2 -> "Catholic",
    3:5 -> "Other,none"
    )
   race2 <- recode(race,
    1 -> "White",
    2 -> "Black"
    )
  })

Having constructed the unordered factors vote3, occup4, relig3, and race2 we can proceed examining the association the vote, occupational class, relgious denomination, and race. First, we look upon a simple contingency table.

ftable(xtabs(~vote3+occup4,data=vote.48))
occup4
vote3 Upper white collar Other white collar Blue collar Farmer
Truman 17 30 114 26
Dewey 67 31 36 14
Other 2 0 4 3

Tables of percentages may seem more informative about the impact of various factors on the vote. So we use the function genTable to obtain such tables of percentages:

gt1 <- genTable(percent(vote3)~occup4,data=vote.48)
## For knitr-ing, we use ```{r, ftable.digits=c(2,2,2,0)} here.
ftable(gt1,row.vars=2)
occup4 Truman Dewey Other N
Upper white collar 19 . 77 77 . 91 2 . 33 86
Other white collar 49 . 18 50 . 82 0 . 00 61
Blue collar 74 . 03 23 . 38 2 . 60 154
Farmer 60 . 47 32 . 56 6 . 98 43

Obviously, voters from farmer and blue collar worker households were especially supportive of President Truman, while voters of upper white collar background largely supported the Republican Candidate Dewey.

gt2 <- genTable(percent(vote3)~relig3,data=vote.48)
ftable(gt2,row.vars=2)
relig3 Truman Dewey Other N
Protestant 44 . 71 50 . 98 4 . 31 255
Catholic 66 . 02 33 . 98 0 . 00 103
Other,none 68 . 18 29 . 55 2 . 27 44

This table shows that Catholics and adherents of other denominations were more supportive of Truman than of Dewey.

gt3 <- genTable(percent(vote3)~race2,data=vote.48)
ftable(gt3,row.vars=2)
race2 Truman Dewey Other N
White 51 . 33 45 . 48 3 . 19 376
Black 64 . 71 35 . 29 0 . 00 17

African Americans apparently supported Truman by a large majority. The number of members of this group in the sample is very small, however, so that such an inference would be very shaky.

gt4 <- genTable(percent(vote3)~total.income,data=vote.48)
ftable(gt4,row.vars=2)
total.income Truman Dewey Other N
UNDER $500 50 . 00 50 . 00 0 . 00 8
$500-$999 61 . 54 38 . 46 0 . 00 13
$1000-1999 64 . 41 32 . 20 3 . 39 59
$2000-2999 66 . 99 30 . 10 2 . 91 103
$3000-3999 47 . 52 48 . 51 3 . 96 101
$4000-4999 45 . 83 50 . 00 4 . 17 48
$5000 AND OVER 31 . 82 68 . 18 0 . 00 66

The table of percentage of vote by income suggests that income had some considerable influence on the choice either of Truman or of Dewey, but the unequal distribution of income categories warrants a more refined analysis that takes into account the uncertainty about the vote percentages. Therefore, the percentages of support for Truman broken down by income shown with confidence intervals:

## For knitr-ing, we use ```{r, ftable.digits=c(2,2,2)} here.
inc.tab <- genTable(percent(vote3,ci=TRUE)~total.income,data=vote.48)
ftable(inc.tab,row.vars=c(3,2))
total.income Percentage lower upper
UNDER $500 Truman 50 . 00 15 . 70 84 . 30
Dewey 50 . 00 15 . 70 84 . 30
Other 0 . 00 0 . 00 36 . 94
$500-$999 Truman 61 . 54 31 . 58 86 . 14
Dewey 38 . 46 13 . 86 68 . 42
Other 0 . 00 0 . 00 24 . 71
$1000-1999 Truman 64 . 41 50 . 87 76 . 45
Dewey 32 . 20 20 . 62 45 . 64
Other 3 . 39 0 . 41 11 . 71
$2000-2999 Truman 66 . 99 57 . 03 75 . 94
Dewey 30 . 10 21 . 45 39 . 92
Other 2 . 91 0 . 60 8 . 28
$3000-3999 Truman 47 . 52 37 . 49 57 . 70
Dewey 48 . 51 38 . 45 58 . 67
Other 3 . 96 1 . 09 9 . 83
$4000-4999 Truman 45 . 83 31 . 37 60 . 83
Dewey 50 . 00 35 . 23 64 . 77
Other 4 . 17 0 . 51 14 . 25
$5000 AND OVER Truman 31 . 82 20 . 89 44 . 44
Dewey 68 . 18 55 . 56 79 . 11
Other 0 . 00 0 . 00 5 . 44

Occupational class is more evenly distributed in the sample, thus it may be possible to obtain more precise estimates of the percentages of support for Truman for occupational classes:

occup.tab <- genTable(percent(vote3,ci=TRUE)~occup4,data=vote.48)
ftable(occup.tab,row.vars=c(3,2))
occup4 Percentage lower upper
Upper white collar Truman 19 . 77 11 . 96 29 . 75
Dewey 77 . 91 67 . 67 86 . 14
Other 2 . 33 0 . 28 8 . 15
Other white collar Truman 49 . 18 36 . 14 62 . 30
Dewey 50 . 82 37 . 70 63 . 86
Other 0 . 00 0 . 00 5 . 87
Blue collar Truman 74 . 03 66 . 35 80 . 75
Dewey 23 . 38 16 .